Prompt engineering
Prompt engineering—which is done by prompt engineers, among others—is the practice of writing clear, structured instructions that guide generative AI models to produce useful and relevant outputs. It’s important because generative AI systems depend heavily on the quality of the prompts they receive. Vague or poorly framed prompts can lead to incorrect or misleading responses, while well-designed prompts help models understand the task, context, and expected output.
Prompt engineering is used across various generative models, including text, image, and code generators, as well as code assistants. By adding the right context, constraints, and formatting cues, prompts can steer models toward domain-specific, accurate answers rather than generic or incorrect ones.
In practice, prompts are made up of a few key parts.
- An instruction that defines the task
- Context that narrows the scope
- Input data that the model should work with
- Guidance on the desired format or style of the response
Prompt engineers often test and refine these elements through iteration, using techniques such as zero-shot, chain-of-thought, one-shot, and few-shot prompting, role-based instructions, and step-by-step reasoning prompts.
